{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Derivatives Analytics with Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**_Chapter 11_**\n",
    "\n",
    "**Wiley Finance (2015)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/images/derivatives_analytics_front.jpg\" alt=\"Derivatives Analytics with Python\" width=\"30%\" align=\"left\" border=\"0\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dr. Yves J. Hilpisch\n",
    "\n",
    "The Python Quants GmbH\n",
    "\n",
    "<a href='mailto:dawp@tpq.io'>dawp@tpq.io</a> | <a href='http://tpq.io'>http://tpq.io</a>\n",
    "\n",
    "Python online training | <a href='http://training.tpq.io'>http://training.tpq.io</a>\n",
    "\n",
    "DX Analytics library | <a href='http://dx-analytics.com'>http://dx-analytics.com</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pylab import plt\n",
    "plt.style.use('seaborn')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chapter 11: Calibration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Short Rate Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run 11_cal/CIR_calibration.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.000320, -0.000130, -0.000130, 0.000070, 0.000430, 0.000830,\n",
       "       0.001830, 0.002510, 0.003380])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_list  # spot rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.000320, -0.000130, -0.000130, 0.000070, 0.000430, 0.000830,\n",
       "       0.001829, 0.002508, 0.003374])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zero_rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.002778, 0.019444, 0.038889, 0.083333, 0.166667, 0.250000,\n",
       "       0.500000, 0.750000, 1.000000])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.000000\n",
      "         Iterations: 270\n",
      "         Function evaluations: 485\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.146849, 0.104511, 0.175197])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opt = CIR_calibration()\n",
    "opt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.000387, -0.000063, 0.000585, 0.000794, 0.001210, 0.001754,\n",
       "       0.002155, 0.002463, 0.002730, 0.002960, 0.003155, 0.003317,\n",
       "       0.003454, 0.003594, 0.003751, 0.003938, 0.004168, 0.004452,\n",
       "       0.004804, 0.005234, 0.005757, 0.006383, 0.007125, 0.007996])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Tefjhh7nllttISUnBZoNRo07myCOHceON13LbbXcwaNAQFi1ayIsvPseTTz7bojarp+Bv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\nNzMzaxAXfjMzswZx4TczM2uQ/wIe/ckVWM9NTAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112e220f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_term_structure()\n",
    "plt.savefig('../images/11_cal/term_structure.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.000320, 0.000015, 0.000347, 0.000677, 0.001006, 0.001332,\n",
       "       0.001656, 0.001977, 0.002297, 0.002614, 0.002930, 0.003243,\n",
       "       0.003554, 0.003863, 0.004169, 0.004474, 0.004776, 0.005076,\n",
       "       0.005374, 0.005670, 0.005963, 0.006254, 0.006544, 0.006831])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CIR_forward_rate(opt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1b7TSfiVjEUkPVVWwaFFsmyDz2Wdbr9Abjyfp1q2KQYOq6Nmzip49K+nZM9gb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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112e4ce10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_calibrated_frc(opt)\n",
    "plt.savefig('../images/11_cal/frc_calibration.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ttxQzbFg5//tfjKuvrmXixDpKS9PvK9KRolzxb2RmZ/D17Hx7Aj3T7eTu1WZ2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1135463c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_zcb_values(opt, 2.0)\n",
    "plt.savefig('../images/11_cal/CIR_zcb_values.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stochastic Volatility Calibration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calibration Procedure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.000000\n",
      "         Iterations: 270\n",
      "         Function evaluations: 485\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1148e5278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%run 11_cal/H93_calibration.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(options)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Strike</th>\n",
       "      <th>Call</th>\n",
       "      <th>Maturity</th>\n",
       "      <th>Put</th>\n",
       "      <th>T</th>\n",
       "      <th>r</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>3175.0</td>\n",
       "      <td>126.8</td>\n",
       "      <td>2014-12-19</td>\n",
       "      <td>78.8</td>\n",
       "      <td>0.219178</td>\n",
       "      <td>0.001349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>3200.0</td>\n",
       "      <td>110.9</td>\n",
       "      <td>2014-12-19</td>\n",
       "      <td>87.9</td>\n",
       "      <td>0.219178</td>\n",
       "      <td>0.001349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>3225.0</td>\n",
       "      <td>96.1</td>\n",
       "      <td>2014-12-19</td>\n",
       "      <td>98.1</td>\n",
       "      <td>0.219178</td>\n",
       "      <td>0.001349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>3250.0</td>\n",
       "      <td>82.3</td>\n",
       "      <td>2014-12-19</td>\n",
       "      <td>109.3</td>\n",
       "      <td>0.219178</td>\n",
       "      <td>0.001349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>3275.0</td>\n",
       "      <td>69.6</td>\n",
       "      <td>2014-12-19</td>\n",
       "      <td>121.6</td>\n",
       "      <td>0.219178</td>\n",
       "      <td>0.001349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>3175.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>2015-03-20</td>\n",
       "      <td>129.2</td>\n",
       "      <td>0.468493</td>\n",
       "      <td>0.003203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>3200.0</td>\n",
       "      <td>156.1</td>\n",
       "      <td>2015-03-20</td>\n",
       "      <td>139.4</td>\n",
       "      <td>0.468493</td>\n",
       "      <td>0.003203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>3225.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>2015-03-20</td>\n",
       "      <td>150.3</td>\n",
       "      <td>0.468493</td>\n",
       "      <td>0.003203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>3250.0</td>\n",
       "      <td>128.5</td>\n",
       "      <td>2015-03-20</td>\n",
       "      <td>161.8</td>\n",
       "      <td>0.468493</td>\n",
       "      <td>0.003203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>3275.0</td>\n",
       "      <td>115.8</td>\n",
       "      <td>2015-03-20</td>\n",
       "      <td>174.0</td>\n",
       "      <td>0.468493</td>\n",
       "      <td>0.003203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>3175.0</td>\n",
       "      <td>82.3</td>\n",
       "      <td>2014-10-17</td>\n",
       "      <td>24.5</td>\n",
       "      <td>0.046575</td>\n",
       "      <td>0.000038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>3200.0</td>\n",
       "      <td>64.3</td>\n",
       "      <td>2014-10-17</td>\n",
       "      <td>31.5</td>\n",
       "      <td>0.046575</td>\n",
       "      <td>0.000038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>3225.0</td>\n",
       "      <td>48.3</td>\n",
       "      <td>2014-10-17</td>\n",
       "      <td>40.5</td>\n",
       "      <td>0.046575</td>\n",
       "      <td>0.000038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>3250.0</td>\n",
       "      <td>34.6</td>\n",
       "      <td>2014-10-17</td>\n",
       "      <td>51.8</td>\n",
       "      <td>0.046575</td>\n",
       "      <td>0.000038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>3275.0</td>\n",
       "      <td>23.5</td>\n",
       "      <td>2014-10-17</td>\n",
       "      <td>65.8</td>\n",
       "      <td>0.046575</td>\n",
       "      <td>0.000038</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Strike   Call   Maturity    Put         T         r\n",
       "38  2014-09-30  3175.0  126.8 2014-12-19   78.8  0.219178  0.001349\n",
       "39  2014-09-30  3200.0  110.9 2014-12-19   87.9  0.219178  0.001349\n",
       "40  2014-09-30  3225.0   96.1 2014-12-19   98.1  0.219178  0.001349\n",
       "41  2014-09-30  3250.0   82.3 2014-12-19  109.3  0.219178  0.001349\n",
       "42  2014-09-30  3275.0   69.6 2014-12-19  121.6  0.219178  0.001349\n",
       "342 2014-09-30  3175.0  171.0 2015-03-20  129.2  0.468493  0.003203\n",
       "343 2014-09-30  3200.0  156.1 2015-03-20  139.4  0.468493  0.003203\n",
       "344 2014-09-30  3225.0  142.0 2015-03-20  150.3  0.468493  0.003203\n",
       "345 2014-09-30  3250.0  128.5 2015-03-20  161.8  0.468493  0.003203\n",
       "346 2014-09-30  3275.0  115.8 2015-03-20  174.0  0.468493  0.003203\n",
       "456 2014-09-30  3175.0   82.3 2014-10-17   24.5  0.046575  0.000038\n",
       "457 2014-09-30  3200.0   64.3 2014-10-17   31.5  0.046575  0.000038\n",
       "458 2014-09-30  3225.0   48.3 2014-10-17   40.5  0.046575  0.000038\n",
       "459 2014-09-30  3250.0   34.6 2014-10-17   51.8  0.046575  0.000038\n",
       "460 2014-09-30  3275.0   23.5 2014-10-17   65.8  0.046575  0.000038"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0 | [2.500 0.010 0.050 -0.750 0.010] | 1383.261 | 500.000\n",
      "  25 | [2.500 0.020 0.050 -0.750 0.010] | 776.135 |  37.017\n",
      "  50 | [2.500 0.020 0.250 -0.750 0.020] | 189.987 |  11.029\n",
      "  75 | [2.500 0.030 0.150 -0.750 0.030] |  73.318 |   7.465\n",
      " 100 | [2.500 0.040 0.050 -0.500 0.010] | 215.473 |   7.465\n",
      " 125 | [2.500 0.040 0.250 -0.500 0.020] |  34.849 |   7.465\n",
      " 150 | [7.500 0.010 0.150 -0.500 0.030] | 344.185 |   7.465\n",
      " 175 | [7.500 0.020 0.050 -0.250 0.010] | 433.526 |   7.465\n",
      " 200 | [7.500 0.020 0.250 -0.250 0.020] | 151.386 |   7.465\n",
      " 225 | [7.500 0.030 0.150 -0.250 0.030] |  87.733 |   7.465\n",
      " 250 | [7.500 0.040 0.050 0.000 0.010] | 171.852 |   7.465\n",
      " 275 | [7.500 0.040 0.250 0.000 0.020] | 227.431 |   7.465\n",
      " 300 | [2.078 0.023 0.220 -0.871 0.028] |   5.897 |   5.683\n",
      " 325 | [1.777 0.024 0.223 -0.900 0.028] |   5.539 |   5.358\n",
      " 350 | [1.518 0.023 0.243 -0.968 0.028] |   5.167 |   5.147\n",
      " 375 | [2.027 0.024 0.237 -0.992 0.028] |   5.043 |   4.997\n",
      " 400 | [2.820 0.024 0.236 -0.998 0.028] |   4.966 |   4.953\n",
      " 425 | [3.437 0.024 0.247 -0.999 0.029] |   4.901 |   4.895\n",
      " 450 | [3.569 0.023 0.252 -1.000 0.029] |   4.890 |   4.889\n",
      " 475 | [3.894 0.023 0.253 -1.000 0.029] |   4.887 |   4.886\n",
      " 500 | [3.701 0.023 0.253 -1.000 0.029] |   4.883 |   4.883\n",
      " 525 | [3.617 0.023 0.263 -0.998 0.029] |   4.834 |   4.834\n",
      " 550 | [7.156 0.024 0.366 -0.975 0.031] |   3.678 |   3.678\n",
      " 575 | [6.839 0.026 0.512 -0.924 0.030] |   2.167 |   1.921\n",
      " 600 | [7.930 0.028 0.615 -0.891 0.030] |   1.693 |   1.693\n",
      " 625 | [9.097 0.027 0.640 -0.893 0.031] |   1.280 |   1.261\n",
      " 650 | [12.310 0.026 0.798 -0.865 0.033] |   0.566 |   0.507\n",
      " 675 | [13.313 0.026 0.837 -0.859 0.034] |   0.485 |   0.485\n",
      " 700 | [15.571 0.026 0.898 -0.850 0.035] |   0.446 |   0.445\n",
      " 725 | [15.458 0.026 0.897 -0.849 0.035] |   0.412 |   0.394\n",
      " 750 | [17.679 0.026 0.957 -0.830 0.035] |   0.332 |   0.332\n",
      " 775 | [18.797 0.026 0.985 -0.818 0.035] |   0.310 |   0.310\n",
      " 800 | [18.613 0.026 0.982 -0.819 0.035] |   0.308 |   0.308\n",
      " 825 | [18.544 0.026 0.980 -0.820 0.035] |   0.307 |   0.307\n",
      " 850 | [18.456 0.026 0.978 -0.820 0.035] |   0.307 |   0.307\n",
      " 875 | [18.441 0.026 0.978 -0.821 0.035] |   0.307 |   0.307\n",
      " 900 | [18.442 0.026 0.978 -0.821 0.035] |   0.307 |   0.307\n",
      " 925 | [18.448 0.026 0.978 -0.821 0.035] |   0.307 |   0.307\n",
      " 950 | [18.447 0.026 0.978 -0.821 0.035] |   0.307 |   0.307\n",
      " 975 | [18.447 0.026 0.978 -0.821 0.035] |   0.307 |   0.307\n",
      "Warning: Maximum number of function evaluations has been exceeded.\n",
      "CPU times: user 3min 38s, sys: 497 ms, total: 3min 39s\n",
      "Wall time: 3min 39s\n"
     ]
    }
   ],
   "source": [
    "%time opt_sv = H93_calibration_full()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([18.447, 0.026, 0.978, -0.821, 0.035])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opt_sv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmpl = r'''\n",
    "\\begin{itemize}\n",
    "    \\item $\\kappa_v = %.3f$\n",
    "    \\item $\\theta_v = %.3f$\n",
    "    \\item $\\sigma_v = %.3f$\n",
    "    \\item $\\rho = %.3f$\n",
    "    \\item $v_0 = %.3f$\n",
    "\\end{itemize}\n",
    "'''\n",
    "results = tmpl % tuple(opt_sv)\n",
    "rf = open('11_cal/H93_results.tex', 'w')\n",
    "rf.writelines(results)\n",
    "rf.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "options['Model'] = H93_calculate_model_values(opt_sv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.074370708113228545"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(options['Model'] - options['Call'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.4923097895529267"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(abs(options['Model'] - options['Call']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/8ThUVflUVfn81V+5Svrnnmu/N3yCBx8sBUoZNChg3rywB79wode2Y1Hputtz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\nIAcSuvo6gK0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116609c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 5))\n",
    "for mat in set(options['Maturity']):\n",
    "    options[options.Maturity == mat].plot(x='Strike', y='Call',\n",
    "                                          style='b', lw=1.5,\n",
    "                                          legend=False, ax=ax)\n",
    "    options[options.Maturity == mat].plot(x='Strike', y='Model',\n",
    "                                          style='ro', legend=False,\n",
    "                                          ax=ax)\n",
    "plt.xlabel('strike')\n",
    "plt.ylabel('option values')\n",
    "plt.grid(True)\n",
    "plt.savefig('../images/11_cal/H93_calibration_quotes.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implied Volatilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "options['Model'] = H93_calculate_model_values(opt_sv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = '11_cal/cal_results_sv.h5'\n",
    "h5 = pd.HDFStore(filename, 'w')\n",
    "h5['options'] = options\n",
    "h5.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.000000\n",
      "         Iterations: 270\n",
      "         Function evaluations: 485\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112ed06a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%run 11_cal/plot_implied_volatilities.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "options = calculate_implied_volatilities(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.00139413807759\n",
      "0.0173881320372\n"
     ]
    }
   ],
   "source": [
    "# total net error\n",
    "print(np.sum(options['model_iv'] - options['market_iv']))\n",
    "# total absolute error\n",
    "print(np.sum(abs(options['model_iv'] - options['market_iv'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mdhi1+0DtQ9BNJSKtWKdOsN9+Kfbbr3Z7iRdfrN5ANMldd5Vw883B9hKDBtVu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\ny/KJzDw/My8BfgWcDxARLwcWZ+ZK4Czg8ojY0Sy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      "text/plain": [
       "<matplotlib.figure.Figure at 0x116adaf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vvORFfQL05puFPPJIEWvWNHzsffjwIOEZMwY23rhHgwSob98OvLEWaDFU6QhR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\n48gCJ4QQQgiN4x+APY2/3y311wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116ada4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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u/n7M1xMR2Ujp3CyvmAYGffI2l1yyeqPyxsb1y3REF25NJ0LpobKXXgp6iLIl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\nMazsd6PUpssAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116be0ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_implied_volatilities(options, 'H93')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Short-Term Index Calibration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calibration Procedure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.000000\n",
      "         Iterations: 270\n",
      "         Function evaluations: 485\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116b02f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%run 11_cal/BCC97_calibration_short.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(options)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0 | [0.000 -0.500 0.000] |   0.651 |   0.651\n",
      "  25 | [0.200 -0.500 0.000] |  40.649 |   0.651\n",
      "  50 | [0.400 -0.500 0.250] | 160.988 |   0.651\n",
      "  75 | [0.000 -0.525 0.000] |   0.651 |   0.646\n",
      " 100 | [0.011 -0.236 0.001] |   0.580 |   0.577\n",
      " 125 | [0.009 -0.532 0.000] |   0.558 |   0.558\n",
      " 150 | [0.008 -0.599 0.000] |   0.558 |   0.558\n",
      " 175 | [0.008 -0.600 0.001] |   0.558 |   0.558\n",
      " 200 | [0.008 -0.600 0.001] |   0.558 |   0.558\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.557747\n",
      "         Iterations: 118\n",
      "         Function evaluations: 220\n",
      "CPU times: user 27.8 s, sys: 78.1 ms, total: 27.8 s\n",
      "Wall time: 27.9 s\n"
     ]
    }
   ],
   "source": [
    "%time opt_jump = BCC_calibration_short()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.008, -0.600, 0.001])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opt_jump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmpl = r'''\n",
    "\\begin{itemize}\n",
    "    \\item $\\lambda = %.3f$\n",
    "    \\item $\\mu = %.3f$\n",
    "    \\item $\\delta = %.3f$\n",
    "\\end{itemize}\n",
    "'''\n",
    "results = tmpl % tuple(opt_jump)\n",
    "rf = open('11_cal/BCC97_jump_results.tex', 'w')\n",
    "rf.writelines(results)\n",
    "rf.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "options['Model'] = BCC_jump_calculate_model_values(opt_jump)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Rt242nTu346uvviAnJ5uvv15Dly7tqVs3m06dHBkZIXJysvnvf79hx44t/PWvfwWgfft2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\nKseAI0mSKseAI0mSKseAI0mSKuf/Jf+lKrKLrJMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1135a4c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_calibration_results(opt_jump)\n",
    "plt.savefig('../images/11_cal/BCC97_jump_calibration_quotes.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Full Index Calibration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calibration Procedure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.000000\n",
      "         Iterations: 270\n",
      "         Function evaluations: 485\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116d8e7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%run 11_cal/BCC97_calibration_full.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(options)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0 | [18.447 0.026 0.978 -0.821 0.035 0.008 -0.600 0.001] |   3.398 |   3.398\n",
      "  25 | [19.073 0.026 0.977 -0.871 0.034 0.008 -0.586 0.001] |   0.820 |   0.820\n",
      "  50 | [19.358 0.025 0.966 -0.883 0.036 0.008 -0.578 0.001] |   0.239 |   0.208\n",
      "  75 | [19.107 0.025 0.968 -0.895 0.035 0.008 -0.580 0.001] |   0.156 |   0.153\n",
      " 100 | [19.107 0.025 0.968 -0.891 0.035 0.008 -0.580 0.001] |   0.152 |   0.151\n",
      " 125 | [19.087 0.025 0.968 -0.903 0.035 0.008 -0.579 0.001] |   0.151 |   0.146\n",
      " 150 | [19.088 0.025 0.967 -0.917 0.035 0.008 -0.577 0.001] |   0.146 |   0.145\n",
      " 175 | [19.301 0.025 0.967 -0.920 0.035 0.008 -0.574 0.001] |   0.141 |   0.141\n",
      " 200 | [20.414 0.025 0.962 -0.964 0.036 0.008 -0.557 0.000] |   0.123 |   0.123\n",
      " 225 | [21.282 0.025 0.960 -0.981 0.036 0.008 -0.546 0.000] |   0.111 |   0.107\n",
      " 250 | [21.497 0.025 0.960 -0.983 0.036 0.008 -0.543 0.000] |   0.107 |   0.106\n",
      " 275 | [21.528 0.025 0.960 -0.985 0.036 0.008 -0.543 0.000] |   0.106 |   0.106\n",
      " 300 | [21.549 0.025 0.960 -0.985 0.036 0.008 -0.542 0.000] |   0.106 |   0.106\n",
      " 325 | [21.555 0.025 0.960 -0.985 0.036 0.008 -0.542 0.000] |   0.106 |   0.106\n",
      " 350 | [21.560 0.025 0.960 -0.985 0.036 0.008 -0.542 0.000] |   0.106 |   0.106\n",
      " 375 | [21.558 0.025 0.960 -0.985 0.036 0.008 -0.542 0.000] |   0.106 |   0.106\n",
      " 400 | [21.578 0.025 0.959 -0.985 0.036 0.008 -0.541 0.000] |   0.106 |   0.106\n",
      " 425 | [21.605 0.025 0.959 -0.985 0.036 0.008 -0.539 0.000] |   0.106 |   0.106\n",
      " 450 | [21.661 0.025 0.959 -0.984 0.036 0.008 -0.537 0.000] |   0.106 |   0.106\n",
      " 475 | [21.717 0.025 0.958 -0.983 0.036 0.008 -0.534 0.000] |   0.106 |   0.105\n",
      " 500 | [21.858 0.025 0.957 -0.983 0.036 0.008 -0.527 0.000] |   0.105 |   0.105\n",
      " 525 | [21.939 0.025 0.956 -0.989 0.036 0.008 -0.520 0.000] |   0.104 |   0.104\n",
      "Warning: Maximum number of function evaluations has been exceeded.\n",
      "CPU times: user 3min 20s, sys: 547 ms, total: 3min 20s\n",
      "Wall time: 3min 21s\n"
     ]
    }
   ],
   "source": [
    "%time opt_full = BCC_calibration_full()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([22.212, 0.025, 0.952, -0.999, 0.036, 0.008, -0.501, 0.000])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opt_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmpl = r'''\n",
    "\\begin{itemize}\n",
    "    \\item $\\kappa_v = %.3f$\n",
    "    \\item $\\theta_v = %.3f$\n",
    "    \\item $\\sigma_v = %.3f$\n",
    "    \\item $\\rho = %.3f$\n",
    "    \\item $v_0 = %.3f$\n",
    "    \\item $\\lambda = %.3f$\n",
    "    \\item $\\mu = %.3f$\n",
    "    \\item $\\delta = %.3f$\n",
    "\\end{itemize}\n",
    "'''\n",
    "results = tmpl % tuple(opt_full)\n",
    "rf = open('11_cal/BCC97_full_results.tex', 'w')\n",
    "rf.writelines(results)\n",
    "rf.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calibrated Option Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "options['Model'] = BCC_calculate_model_values(opt_full)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9Fi1KpBO8MaqmFxHpSk8S+lpr7YutDxhjvh3S+UieazP1Ys9uWmZV0HTRJaxZ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\nRCQP/D8GWmNxILeevQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116c47208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 5))\n",
    "for mat in set(options['Maturity']):\n",
    "    options[options.Maturity == mat].plot(x='Strike', y='Call',\n",
    "                                          style='b', lw=1.5,\n",
    "                                          legend=False, ax=ax)\n",
    "    options[options.Maturity == mat].plot(x='Strike', y='Model',\n",
    "                                          style='ro', legend=False,\n",
    "                                          ax=ax)\n",
    "plt.xlabel('strike')\n",
    "plt.ylabel('option values')\n",
    "plt.grid(True)\n",
    "plt.savefig('../images/11_cal/BCC97_full_calibration_quotes.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = '11_cal/cal_results_full.h5'\n",
    "h5 = pd.HDFStore(filename, 'w')\n",
    "h5['options'] = options\n",
    "h5.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implied Volatilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112e4c908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%run 11_cal/plot_implied_volatilities.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "options = calculate_implied_volatilities(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.000916384905828\n",
      "0.00934023531612\n"
     ]
    }
   ],
   "source": [
    "# total net error\n",
    "print(np.sum(options['model_iv'] - options['market_iv']))\n",
    "# total absolute error\n",
    "print(np.sum(abs(options['model_iv'] - options['market_iv'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OSnhEpOXFneCcC9wG7EmwJtSTwDdjvqeIRKxjR9hjjyCRAdi4EV54oYA5c4Jx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\nVDsWOJIkqXYscCRJUu1Y4EiSpNr5P0OmJYUY6XNwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116c47ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cM9vN3V+Kuh0iIrmUSMCgQUkGDUqy117Nh74++ohG21zUJ0AvvljI/fc33+ur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\nD3DMzMyscjzAMTMzs8r5Gx8XSG5dhYocAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116c47390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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K905EREQKa+3hR1BPMOemdMGbrB89hsbJZ3Rq/g0EbxP/4INl/O1v9/PFL+7DokX/4uGH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\nBY4kSWodCxxJktQ6/wcL0hXx4d1KZAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116c25da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_implied_volatilities(options, 'BCC97')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implied Volatility Calibration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.000000\n",
      "         Iterations: 270\n",
      "         Function evaluations: 485\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11487b4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%run 11_cal/BCC97_calibration_iv.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(options)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0 | [22.212 0.025 0.952 -0.999 0.036 0.008 -0.501 0.000] |   0.102 |   0.102\n",
      "  25 | [22.379 0.025 0.966 -0.997 0.036 0.008 -0.505 0.000] |   0.100 |   0.100\n",
      "  50 | [22.615 0.025 0.971 -0.992 0.036 0.008 -0.515 0.000] |   0.098 |   0.094\n",
      "  75 | [23.543 0.025 1.001 -0.980 0.036 0.008 -0.536 0.000] |   0.082 |   0.082\n",
      " 100 | [26.268 0.025 1.111 -0.934 0.038 0.008 -0.585 0.000] |   0.057 |   0.053\n",
      " 125 | [27.073 0.025 1.145 -0.918 0.038 0.009 -0.591 0.000] |   0.053 |   0.053\n",
      " 150 | [27.334 0.024 1.156 -0.914 0.039 0.009 -0.598 0.000] |   0.053 |   0.053\n",
      " 175 | [27.443 0.024 1.159 -0.913 0.039 0.009 -0.600 0.000] |   0.053 |   0.052\n",
      " 200 | [27.446 0.024 1.159 -0.913 0.039 0.009 -0.600 0.000] |   0.052 |   0.052\n",
      " 225 | [27.476 0.024 1.160 -0.913 0.039 0.009 -0.600 0.000] |   0.052 |   0.052\n",
      " 250 | [27.735 0.025 1.166 -0.915 0.039 0.008 -0.599 0.000] |   0.052 |   0.052\n",
      " 275 | [28.197 0.025 1.174 -0.920 0.039 0.008 -0.597 0.000] |   0.052 |   0.052\n",
      " 300 | [28.055 0.025 1.169 -0.923 0.039 0.008 -0.595 0.000] |   0.052 |   0.051\n",
      " 325 | [28.152 0.025 1.172 -0.924 0.039 0.008 -0.595 0.000] |   0.051 |   0.051\n",
      " 350 | [28.270 0.025 1.176 -0.924 0.039 0.008 -0.596 0.000] |   0.051 |   0.051\n",
      " 375 | [28.678 0.025 1.187 -0.926 0.039 0.007 -0.598 0.000] |   0.050 |   0.050\n",
      " 400 | [28.997 0.025 1.196 -0.929 0.039 0.007 -0.599 0.000] |   0.050 |   0.050\n",
      " 425 | [28.827 0.025 1.192 -0.929 0.039 0.007 -0.599 0.000] |   0.050 |   0.050\n",
      " 450 | [28.467 0.025 1.183 -0.928 0.039 0.007 -0.600 0.000] |   0.049 |   0.049\n",
      " 475 | [28.399 0.025 1.179 -0.936 0.039 0.007 -0.600 0.000] |   0.048 |   0.048\n",
      " 500 | [28.356 0.025 1.174 -0.950 0.039 0.007 -0.599 0.000] |   0.046 |   0.046\n",
      " 525 | [28.335 0.025 1.172 -0.952 0.039 0.007 -0.599 0.000] |   0.046 |   0.046\n",
      "Warning: Maximum number of function evaluations has been exceeded.\n",
      "CPU times: user 3min 30s, sys: 528 ms, total: 3min 31s\n",
      "Wall time: 3min 31s\n"
     ]
    }
   ],
   "source": [
    "%time opt_iv = BCC_iv_calibration_full()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([28.473, 0.025, 1.175, -0.953, 0.039, 0.007, -0.600, 0.000])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opt_iv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmpl = r'''\n",
    "\\begin{itemize}\n",
    "    \\item $\\kappa_v = %.3f$\n",
    "    \\item $\\theta_v = %.3f$\n",
    "    \\item $\\sigma_v = %.3f$\n",
    "    \\item $\\rho = %.3f$\n",
    "    \\item $v_0 = %.3f$\n",
    "    \\item $\\lambda = %.3f$\n",
    "    \\item $\\mu = %.3f$\n",
    "    \\item $\\delta = %.3f$\n",
    "\\end{itemize}\n",
    "'''\n",
    "results = tmpl % tuple(opt_iv)\n",
    "rf = open('11_cal/BCC97_iv_results.tex', 'w')\n",
    "rf.writelines(results)\n",
    "rf.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calibrated Option Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "options['Model'] = BCC_calculate_model_values(opt_iv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HlO0Au+P98ne1MmZaIbNn28ydazNvns3s2TZnnJHiBgshRJbqyRj6OODu6L/D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\nknrAhC5JUg/8A/aV98xob5OLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1175a6908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 5))\n",
    "for mat in set(options['Maturity']):\n",
    "    options[options.Maturity == mat].plot(x='Strike', y='Call',\n",
    "                                          style='b', lw=1.5,\n",
    "                                          legend=False, ax=ax)\n",
    "    options[options.Maturity == mat].plot(x='Strike', y='Model',\n",
    "                                          style='ro', legend=False,\n",
    "                                          ax=ax)\n",
    "plt.xlabel('strike')\n",
    "plt.ylabel('option values')\n",
    "plt.grid(True)\n",
    "plt.savefig('../images/11_cal/BCC97_iv_calibration_quotes.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = '11_cal/cal_results_full_iv.h5'\n",
    "h5 = pd.HDFStore(filename, 'w')\n",
    "h5['options'] = options\n",
    "h5.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calibrated Implied Volatilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "options = calculate_implied_volatilities(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.00073362732517\n",
      "0.0070137001147\n"
     ]
    }
   ],
   "source": [
    "# total net error\n",
    "print(np.sum(options['model_iv'] - options['market_iv']))\n",
    "# total absolute error\n",
    "print(np.sum(abs(options['model_iv'] - options['market_iv'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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E5zzgT8D+BGtCPQV8J+Z7ikjE2reH/fYLEhmALVvgxRcLmD8/GMfz17+24847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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116bc54a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Wpw+MHp1m9Oj8hr4yX/fd14u1axuPZQ0cGBQ8Y8fC4MG9NfQlPU5cPTg/IFgR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\nIpOAO2sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1175a6c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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BS4AlwGjgbHdf3713IiIi0r5Nx5/AOoI1MZWLX2fr2HG0TDsr7/UxEOx+vWrVSv7wh9/z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\nRCYiIiJqK4lMRERE1FYSmYiIiKit/wdLlVQWZifvhwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116bcf1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_implied_volatilities(options, 'BCC97_iv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>\n",
    "\n",
    "<a href=\"http://tpq.io\" target=\"_blank\">http://tpq.io</a> | <a href=\"http://twitter.com/dyjh\" target=\"_blank\">@dyjh</a> | <a href=\"mailto:training@tpq.io\">training@tpq.io</a>\n",
    "\n",
    "**Quant Platform** |\n",
    "<a href=\"http://quant-platform.com\">http://quant-platform.com</a>\n",
    "\n",
    "**Python for Finance** |\n",
    "<a href=\"http://python-for-finance.com\" target=\"_blank\">Python for Finance @ O'Reilly</a>\n",
    "\n",
    "**Derivatives Analytics with Python** |\n",
    "<a href=\"http://derivatives-analytics-with-python.com\" target=\"_blank\">Derivatives Analytics @ Wiley Finance</a>\n",
    "\n",
    "**Listed Volatility and Variance Derivatives** |\n",
    "<a href=\"http://lvvd.tpq.io\" target=\"_blank\">Listed VV Derivatives @ Wiley Finance</a>\n",
    "\n",
    "**Python Online Training** |\n",
    "<a href=\"http://certificate.tpq.io\" target=\"_blank\">Python for Algorithmic Trading University Certificate</a>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
